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Freedom251 › Blog › AI coding agents

How to Review and Safely Run Code Changes Made by a Coding Agent

By Freedom251 Team · · 12 min read

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The safe default is simple: do not run an agent’s patch just because its explanation sounds convincing. First establish what the change was meant to do, then inspect the complete diff—including instructions, dependencies, tests, CI and deployment files. Execute it only in a disposable, least-privilege environment without production secrets. Run the relevant tests and security checks, inspect the final diff again, and have a qualified human approve the result before commit, merge or deployment.

AI authorship does not prove that code is unsafe, and it does not prove that code is correct. The review process must be based on the patch, its execution boundary, evidence from tests and scans, and the project’s acceptance and security requirements.

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What makes an agent-generated change different?

A coding agent can edit source files, add dependencies, change build settings, run commands and consume repository content. That broad capability creates two separate questions:

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  • Does the patch satisfy the requested behavior?
  • Could the patch, its dependencies or its execution expose the system to harm?

A polished pull-request summary answers neither question by itself. The summary may omit an out-of-scope file, misunderstand a requirement or report a test that did not cover the important behavior. Treat it as a useful index to investigate, not as evidence that the change is ready.

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Reviewers should also distinguish the patch from the environment in which it runs. A harmless-looking script can still read mounted credentials, alter files outside the workspace or send data over the network. Conversely, a tightly sandboxed run limits access but cannot tell whether the implementation matches product intent. Both code correctness and execution safety require separate checks.

Start by defining the intended change

Confirm the repository and target

Before opening individual files, verify the repository, branch or checkout, requested issue, and expected acceptance criteria. Confirm that you are reviewing the intended commit or pull request and note any unresolved merge conflicts. A simple status check can prevent reviewing the wrong workspace:

git status --short
 git branch --show-current

Record the behavior that must change, the behavior that must remain unchanged, and any explicit non-goals. If the request is vague, ask for clarification before running generated code. A reviewer cannot verify an implementation against an undefined outcome.

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Use the explanation only as a map

Read the agent’s summary and test report, but use them to locate evidence in the repository. Check that each claimed file is actually changed and that every acceptance criterion has a corresponding implementation or test. Open the relevant lines rather than relying on a bullet such as “improved validation.”

OpenAI’s pull-request guidance states: “Review generated findings against the relevant code before relying on them.” The same principle applies to an agent’s own description: trace claims to code, test output and configuration.

Inspect the complete diff before executing anything

Review every changed file

Begin with a summary, then inspect the complete patch:

git diff --stat
git diff --check
git diff

Do not stop at application code. Include new, deleted and renamed files, generated artifacts, lockfiles and files changed only for formatting. Pay particular attention to:

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  • Authentication, authorization, session and identity code.
  • Input validation, deserialization, cryptography and access-control checks.
  • CI/CD workflows, deployment manifests, infrastructure and container definitions.
  • Build scripts, package-manager configuration and tool or MCP-server configuration.
  • Tests that were removed, weakened, skipped or made non-deterministic.
  • Environment-variable handling, credential paths, logging and telemetry.
  • Repository instruction files such as AGENTS.md and similar persistent rule files.

Look for changes outside the requested scope. A lockfile update may pull in many transitive packages; a workflow edit may grant a job a new token; a “cleanup” can remove a security check. If a file changed, the author should be able to explain why.

Check the final state, not only the initial patch

Tests, formatters and agent fixes can modify files after the first review. Capture the diff before execution, run the permitted checks, then inspect the working tree and diff again. Review the version that would actually be committed, including any generated files and lockfile changes.

Treat repository instructions and content as untrusted input

Agent-readable content is part of the attack surface. Issue text, pull-request comments, README files, dependency changelogs, fetched web pages, tool responses and persistent instruction files can contain directions that influence an agent. Those directions may be accidental, stale or deliberately crafted to make the agent reveal secrets, weaken safeguards or modify unrelated files.

Review persistent instructions as security-sensitive configuration

Open AGENTS.md and equivalent files in the repository and its parent directories. Check who added or changed them, what commands they authorize, and whether they silently override the task’s scope. A new rule such as “upload logs to an external service” or “disable the security scan” deserves the same scrutiny as executable code.

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Do not assume that an instruction embedded in a README or comment is trustworthy merely because it is written in a familiar format. Separate the user’s stated requirement from repository text and from instructions returned by tools. If those sources conflict, pause and resolve the conflict with a human maintainer.

Watch for instruction-hijacking symptoms

  • A request to print, copy or upload environment variables, tokens, SSH keys or credential files.
  • A direction to bypass review, disable tests or ignore a security warning.
  • Commands that fetch and execute remote content without a pinned, reviewed source.
  • Unexpected edits to instruction files, hooks, CI policies or access-control configuration.
  • Claims that a secret is needed when the task can be completed without it.

Audit dependencies and surrounding configuration

Verify every new package

Inspect package manifests and lockfiles together. Confirm that each new dependency is intentional, correctly named and compatible with the project’s supported runtime. AI systems can invent plausible dependency names or select a similarly named package with different ownership. Check the package source, version, license and transitive additions using the project’s normal dependency-review process before installation.

Pin versions according to the project’s policy and preserve integrity information in the lockfile. Do not accept a broad version range simply because it makes the build pass. If a dependency is unnecessary, remove it rather than adding a new supply-chain obligation.

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Review permissions and operational changes

Compare CI and deployment permissions before and after the patch. Ask whether a workflow now receives write access, a deployment key, a cloud role or access to pull-request content from untrusted contributors. Check container users, mounted volumes, exposed ports, network policies and runtime capabilities. A change that broadens access is security-sensitive even when the application code is small.

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Inspect secrets handling

Search the diff and relevant configuration for hard-coded tokens, private keys, passwords, connection strings and accidentally committed local settings. Check that logs do not print secrets and that error messages do not expose sensitive request data. Run secret scanning as a normal gate, not only when a leak is suspected. If a real credential appears in the patch or execution logs, stop, revoke or rotate it through the organization’s incident process, remove it from history as required, and investigate where it may have been copied.

Build a bounded environment before running generated code

Run an agent’s code in a dev container, restricted shell, virtual machine or disposable checkout whenever practical. The environment should contain only the files, tools and credentials required for the task.

Boundary Safer setup What to verify
Filesystem Disposable workspace with read/write access limited to the project No home directory, SSH folder, production mounts or unrelated repositories are visible
Privilege Unprivileged user and no administrative escalation Commands cannot install system packages or alter host security settings without approval
Network Outbound access disabled or allowlisted for the task Build scripts cannot silently upload source, contact arbitrary hosts or download unreviewed executables
Credentials No production, organization-wide or long-lived secrets Environment variables, credential stores and deployment keys are absent or task-scoped
Tools Only required interpreters, package managers and approved integrations Unreviewed MCP servers, browser sessions and external tools are blocked
Audit Recorded commands, approvals, network decisions and outputs You can reconstruct what ran and what files or services it touched

Use short-lived, narrowly scoped credentials only when access is genuinely required. Approval controls and sandboxing protect different boundaries: an approval prompt may stop a dangerous command, while a sandbox limits what an approved command can reach. Neither control establishes that the patch is correct.

Inspect commands before allowing them

Read package-manager scripts, build hooks, test fixtures and shell commands before execution. Pay special attention to commands that use curl, wget, shell evaluation, archive extraction, filesystem deletion, cloud CLIs or database migrations. A test command can execute installation hooks or contact external services. If a command is not needed to validate the requested behavior, do not run it.

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Follow a safe execution sequence

  1. Create a branch or disposable checkout. Preserve the original state so generated changes and test artifacts can be discarded.
  2. Capture the initial state. Record the repository, commit, branch, working-tree status and complete diff.
  3. Review the runtime boundary. Check filesystem permissions, mounted volumes, network policy, available tools, environment variables and current user privileges.
  4. Remove production access. Unset unnecessary variables and disconnect deployment credentials, SSH keys, cloud roles and organization secrets.
  5. Install only reviewed dependencies. Inspect manifests and lockfiles first; use the project’s approved, pinned installation method.
  6. Run the smallest relevant checks first. Start with deterministic unit or static checks that do not require external services. Expand to integration or end-to-end tests only when the environment and data are safe.
  7. Observe side effects. Watch for unexpected network connections, file writes, subprocesses, privilege requests, database changes and generated artifacts.
  8. Run security gates. Use the project’s SAST, software-composition or dependency checks and secret scanning. Add targeted checks for the changed security properties.
  9. Reinspect the diff. Tests and formatters may have changed files. Review the final patch, lockfiles and generated output.
  10. Obtain qualified human approval. A human who understands the code and its threat model must verify the result before commit, merge or deployment.

Test behavior, not just whether the build is green

Map tests to acceptance criteria

For each requested behavior, identify a test that demonstrates it and a test that protects important existing behavior. Include normal, boundary and failure cases. Check test data and assertions: a test that merely exercises a function without checking its result can pass while the feature is broken.

A passing suite establishes only the properties covered by those tests. It does not prove that untested inputs are safe, that deployment settings are correct or that no secret was exposed. Record which checks ran, which were skipped and why.

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Apply security-focused verification

Use the project’s normal static application-security testing, software-composition analysis and secret scanning. For changes to authentication or authorization, test denied as well as allowed requests, including cross-tenant and privilege-boundary cases. For input parsers and deserialization, test malformed, oversized and unexpected data. For cryptography, verify algorithm selection, key handling, nonce or randomness requirements and failure behavior. For CI/CD, deployment, sandbox and network-policy changes, inspect effective permissions and test that prohibited actions remain prohibited.

OWASP’s AI Software Verification Standard recommends that AI-generated code always go through code review by a qualified human engineer. It also calls for security scans and elevated review of critical files. These are verification controls, not a certification that any product or agent automatically meets them.

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Use AI review as an aid, never as the approval authority

A second agent can help find missing validation, suspicious data flows or inconsistent tests, but its output is another set of claims to verify. Ask for concrete evidence: file names, line ranges, threat scenarios, expected impact and a reproducer or test. Then inspect the cited code yourself.

Do not accept a “no findings” result as proof of safety. Automated review can miss business-logic errors, environment-specific permissions, supply-chain attacks and requirements that were never expressed in the prompt. Conversely, a proposed fix may introduce a new defect. OpenAI’s Codex Security documentation describes proposed fixes as requiring human review and recommends the normal review process for generated patch pull requests.

Keep the review focused on the relevant code and output. Do not paste secrets, proprietary data or production logs into an external reviewer. If a review tool needs repository access, apply the same least-privilege, network and audit controls as the coding agent.

Make the merge decision from the final evidence

Before committing or merging, confirm all of the following:

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  • The repository, target branch and requested behavior are unambiguous.
  • Every changed file, instruction file, dependency and configuration edit has an explanation.
  • Out-of-scope changes, unresolved conflicts and weakened or deleted tests are resolved.
  • Dependencies are legitimate, reviewed, pinned according to policy and represented correctly in the lockfile.
  • No production or organization credentials were available to the run, and no secrets appear in code or logs.
  • The final patch passes relevant functional and security checks in the bounded environment.
  • Security-sensitive paths have targeted tests and elevated review.
  • The reviewer understands residual risks, skipped checks and deployment assumptions.
  • A qualified human has reviewed the final result after all agent fixes.

OpenAI’s pull-request guidance is explicit: “Review the result before submitting comments, committing changes, or merging.” If you cannot explain what the patch does, what it can access and what evidence supports it, do not merge it yet.

How to compare execution environments

When selecting a container, virtual machine, hosted runner or agent platform, compare controls rather than relying on a vendor label or a claim that an environment is “secure.” The useful axes are:

Question Evidence to request
Workspace boundary Can the agent read or write outside the checkout, including host and home directories?
Command approval Which commands require approval, and can policy prevent privilege escalation or destructive operations?
Network egress Can outbound traffic be disabled or restricted by destination, protocol and task?
Secret access Are environment variables, credential stores, deployment keys and cloud roles isolated and short-lived?
Tool and MCP control Can integrations be limited to reviewed, pinned servers and minimum permissions?
Sensitive-file gates Are changes to authentication, CI, deployment, dependencies and instruction files flagged for review?
Auditability Are prompts, approvals, tool calls, results, file changes and network decisions recorded?

These dimensions describe safeguards, not a benchmark proving that one provider or product is safer than another. Product features and plan availability can change, so verify current controls in the documentation for the environment you intend to use.

Common failure modes and recovery

The agent changed more than requested

Stop before running the patch. Classify each extra file as required, incidental or unexplained. Revert incidental edits and ask for a narrower change. Treat unexplained CI, dependency, instruction or permission changes as security-sensitive until reviewed.

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A test passes only with broad network or secret access

Do not provide production credentials to make the test green. Determine the external contract, replace live services with fixtures or a local test double where appropriate, and use a scoped test credential only after a human approves the boundary. Document what remains untested.

The dependency scanner reports a new package

Stop installation, verify the package identity and provenance, inspect transitive dependencies and check whether the feature can be implemented without it. Remove hallucinated or unnecessary packages and regenerate the lockfile through the project’s approved process.

A secret appears in the diff or logs

Stop execution and assume the credential may be compromised. Revoke or rotate it, preserve relevant evidence, remove the secret from the working tree and history as required, and investigate logs, caches and external services for copies. Resume only after the incident is contained.

Security review and functional tests disagree

Do not merge on the strength of the green build. Reproduce the security finding, determine whether the test is incomplete or the implementation is unsafe, and obtain review from someone qualified in the affected area. Resolve the conflict with evidence, not by disabling the warning.

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Bottom line: trust the process, not the authorship label

AI-generated code can be acceptable, but only when it passes the same—or stricter—engineering and security gates as human-written code. Read the full diff, treat instructions and dependencies as untrusted inputs, execute within a least-privilege boundary, test the behavior and security properties that matter, and review the final result yourself. A sandbox reduces blast radius; tests provide limited evidence; neither replaces a qualified human decision before merge or deployment.

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